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train | get_model_meta | Get model meta.json from a directory path and validate its contents.
path (unicode or Path): Path to model directory.
RETURNS (dict): The model's meta data. | spacy/util.py | def get_model_meta(path):
"""Get model meta.json from a directory path and validate its contents.
path (unicode or Path): Path to model directory.
RETURNS (dict): The model's meta data.
"""
model_path = ensure_path(path)
if not model_path.exists():
raise IOError(Errors.E052.format(path=... | def get_model_meta(path):
"""Get model meta.json from a directory path and validate its contents.
path (unicode or Path): Path to model directory.
RETURNS (dict): The model's meta data.
"""
model_path = ensure_path(path)
if not model_path.exists():
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train | get_package_path | Get the path to an installed package.
name (unicode): Package name.
RETURNS (Path): Path to installed package. | spacy/util.py | def get_package_path(name):
"""Get the path to an installed package.
name (unicode): Package name.
RETURNS (Path): Path to installed package.
"""
name = name.lower() # use lowercase version to be safe
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"""Get the path to an installed package.
name (unicode): Package name.
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train | get_entry_points | Get registered entry points from other packages for a given key, e.g.
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key (unicode): Entry point name.
RETURNS (dict): Entry points, keyed by name. | spacy/util.py | def get_entry_points(key):
"""Get registered entry points from other packages for a given key, e.g.
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key (unicode): Entry point name.
RETURNS (dict): Entry points, keyed by name.
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result = {}
for entry_point in pkg_resource... | def get_entry_points(key):
"""Get registered entry points from other packages for a given key, e.g.
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key (unicode): Entry point name.
RETURNS (dict): Entry points, keyed by name.
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train | get_entry_point | Check if registered entry point is available for a given name and
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key (unicode): Entry point name.
value (unicode): Name of entry point to load.
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"""Check if registered entry point is available for a given name and
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key (unicode): Entry point name.
value (unicode): Name of entry point to load.
RETURNS: The loaded entry point or None.
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train | is_in_jupyter | Check if user is running spaCy from a Jupyter notebook by detecting the
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RETURNS (bool): True if in Jupyter, False if not. | spacy/util.py | def is_in_jupyter():
"""Check if user is running spaCy from a Jupyter notebook by detecting the
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RETURNS (bool): True if in Jupyter, False if not.
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try:
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"""Check if user is running spaCy from a Jupyter notebook by detecting the
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train | compile_suffix_regex | Compile a sequence of suffix rules into a regex object.
entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search. | spacy/util.py | def compile_suffix_regex(entries):
"""Compile a sequence of suffix rules into a regex object.
entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
"""
expression = "|".join([piece + "$" f... | def compile_suffix_regex(entries):
"""Compile a sequence of suffix rules into a regex object.
entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
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train | compile_infix_regex | Compile a sequence of infix rules into a regex object.
entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer. | spacy/util.py | def compile_infix_regex(entries):
"""Compile a sequence of infix rules into a regex object.
entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
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expression = "|".join([piece for piece ... | def compile_infix_regex(entries):
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train | update_exc | Update and validate tokenizer exceptions. Will overwrite exceptions.
base_exceptions (dict): Base exceptions.
*addition_dicts (dict): Exceptions to add to the base dict, in order.
RETURNS (dict): Combined tokenizer exceptions. | spacy/util.py | def update_exc(base_exceptions, *addition_dicts):
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base_exceptions (dict): Base exceptions.
*addition_dicts (dict): Exceptions to add to the base dict, in order.
RETURNS (dict): Combined tokenizer exceptions.
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exc = dict(... | def update_exc(base_exceptions, *addition_dicts):
"""Update and validate tokenizer exceptions. Will overwrite exceptions.
base_exceptions (dict): Base exceptions.
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train | expand_exc | Find string in tokenizer exceptions, duplicate entry and replace string.
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excs (dict): Tokenizer exceptions.
search (unicode): String to find and replace.
replace (unicode): Replacement.
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excs (dict): Tokenizer exceptions.
search (unicode): String to find and replace.
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train | minibatch | Iterate over batches of items. `size` may be an iterator,
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"""Iterate over batches of items. `size` may be an iterator,
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if isinstance(size, int):
size_ = itertools.repeat(size)
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train | compounding | Yield an infinite series of compounding values. Each time the
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EXAMPLE:
>>> sizes = compounding(1., 10., 1.5)
>>> assert next(sizes) == 1.
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"""Yield an infinite series of compounding values. Each time the
generator is called, a value is produced by multiplying the previous
value by the compound rate.
EXAMPLE:
>>> sizes = compounding(1., 10., 1.5)
>>> assert next(sizes) == 1.
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train | stepping | Yield an infinite series of values that step from a start value to a
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After the final value is reached, the generator continues yielding that
value.
EXAMPLE:
>>> sizes = stepping(1., 200., 100)
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"""Yield an infinite series of values that step from a start value to a
final value over some number of steps. Each step is (stop-start)/steps.
After the final value is reached, the generator continues yielding that
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EXAMPLE:
>>> sizes = stepping(1., 2... | def stepping(start, stop, steps):
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train | decaying | Yield an infinite series of linearly decaying values. | spacy/util.py | def decaying(start, stop, decay):
"""Yield an infinite series of linearly decaying values."""
curr = float(start)
while True:
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curr -= (decay) | def decaying(start, stop, decay):
"""Yield an infinite series of linearly decaying values."""
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train | minibatch_by_words | Create minibatches of a given number of words. | spacy/util.py | def minibatch_by_words(items, size, tuples=True, count_words=len):
"""Create minibatches of a given number of words."""
if isinstance(size, int):
size_ = itertools.repeat(size)
else:
size_ = size
items = iter(items)
while True:
batch_size = next(size_)
batch = []
... | def minibatch_by_words(items, size, tuples=True, count_words=len):
"""Create minibatches of a given number of words."""
if isinstance(size, int):
size_ = itertools.repeat(size)
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train | itershuffle | Shuffle an iterator. This works by holding `bufsize` items back
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From https://gist.github.com/andres-erbsen/1307752
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From https://gist.github.com/andres-erbsen/1307752
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"""Shuffle an iterator. This works by holding `bufsize` items back
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train | validate_json | Validate data against a given JSON schema (see https://json-schema.org).
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validator (jsonschema.DraftXValidator): The validator.
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train | get_serialization_exclude | Helper function to validate serialization args and manage transition from
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train | EntityRuler.labels | All labels present in the match patterns.
RETURNS (set): The string labels.
DOCS: https://spacy.io/api/entityruler#labels | spacy/pipeline/entityruler.py | def labels(self):
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all_labels = set(self.token_patterns.keys())
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train | EntityRuler.patterns | Get all patterns that were added to the entity ruler.
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DOCS: https://spacy.io/api/entityruler#patterns | spacy/pipeline/entityruler.py | def patterns(self):
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all_patterns = []
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RETURNS (list): The original patterns, one dictionary per pattern.
DOCS: https://spacy.io/api/entityruler#patterns
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all_patterns = []
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train | EntityRuler.add_patterns | Add patterns to the entitiy ruler. A pattern can either be a token
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{'label': 'ORG', 'pattern': 'Apple'}
{'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]}
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{'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]}
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train | EntityRuler.from_bytes | Load the entity ruler from a bytestring.
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**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes | spacy/pipeline/entityruler.py | def from_bytes(self, patterns_bytes, **kwargs):
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**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
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train | EntityRuler.from_disk | Load the entity ruler from a file. Expects a file containing
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path (unicode / Path): The JSONL file to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: ht... | spacy/pipeline/entityruler.py | def from_disk(self, path, **kwargs):
"""Load the entity ruler from a file. Expects a file containing
newline-delimited JSON (JSONL) with one entry per line.
path (unicode / Path): The JSONL file to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRu... | def from_disk(self, path, **kwargs):
"""Load the entity ruler from a file. Expects a file containing
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path (unicode / Path): The JSONL file to load.
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train | EntityRuler.to_disk | Save the entity ruler patterns to a directory. The patterns will be
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path (unicode / Path): The JSONL file to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://sp... | spacy/pipeline/entityruler.py | def to_disk(self, path, **kwargs):
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path (unicode / Path): The JSONL file to load.
**kwargs: Other config paramters, mostly for consistency.
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train | read_data | Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
include Doc objects created using nlp.make_doc and then aligned against
the gold-standard sequences. If oracle_segments=True, include Doc objects
created from the gold-standard segments. At least one must be True. | bin/ud/ud_train.py | def read_data(
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limit=None,
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train | golds_to_gold_tuples | Get out the annoying 'tuples' format used by begin_training, given the
GoldParse objects. | bin/ud/ud_train.py | def golds_to_gold_tuples(docs, golds):
"""Get out the annoying 'tuples' format used by begin_training, given the
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tuples = []
for doc, gold in zip(docs, golds):
text = doc.text
ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
sents = [((ids, words... | def golds_to_gold_tuples(docs, golds):
"""Get out the annoying 'tuples' format used by begin_training, given the
GoldParse objects."""
tuples = []
for doc, gold in zip(docs, golds):
text = doc.text
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train | like_num | check if text resembles a number | spacy/lang/fa/lex_attrs.py | def like_num(text):
"""
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text = (
text.replace(",", "")
.replace(".", "")
.replace("،", "")
.replace("٫", "")
.replace("/", "")
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if text.isdigit():
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if text in _num_words:
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... | def like_num(text):
"""
check if text resembles a number
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text = (
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return True
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train | merge_bytes | Concatenate multiple serialized binders into one byte string. | spacy/tokens/_serialize.py | def merge_bytes(binder_strings):
"""Concatenate multiple serialized binders into one byte string."""
output = None
for byte_string in binder_strings:
binder = Binder().from_bytes(byte_string)
if output is None:
output = binder
else:
output.merge(binder)
re... | def merge_bytes(binder_strings):
"""Concatenate multiple serialized binders into one byte string."""
output = None
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binder = Binder().from_bytes(byte_string)
if output is None:
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train | Binder.add | Add a doc's annotations to the binder for serialization. | spacy/tokens/_serialize.py | def add(self, doc):
"""Add a doc's annotations to the binder for serialization."""
array = doc.to_array(self.attrs)
if len(array.shape) == 1:
array = array.reshape((array.shape[0], 1))
self.tokens.append(array)
spaces = doc.to_array(SPACY)
assert array.shape[0... | def add(self, doc):
"""Add a doc's annotations to the binder for serialization."""
array = doc.to_array(self.attrs)
if len(array.shape) == 1:
array = array.reshape((array.shape[0], 1))
self.tokens.append(array)
spaces = doc.to_array(SPACY)
assert array.shape[0... | [
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train | Binder.get_docs | Recover Doc objects from the annotations, using the given vocab. | spacy/tokens/_serialize.py | def get_docs(self, vocab):
"""Recover Doc objects from the annotations, using the given vocab."""
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
for tokens, spaces in zip(self.tokens, self.spaces):
words = [vocab.strings[orth] for orth... | def get_docs(self, vocab):
"""Recover Doc objects from the annotations, using the given vocab."""
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
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train | Binder.merge | Extend the annotations of this binder with the annotations from another. | spacy/tokens/_serialize.py | def merge(self, other):
"""Extend the annotations of this binder with the annotations from another."""
assert self.attrs == other.attrs
self.tokens.extend(other.tokens)
self.spaces.extend(other.spaces)
self.strings.update(other.strings) | def merge(self, other):
"""Extend the annotations of this binder with the annotations from another."""
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self.spaces.extend(other.spaces)
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train | Binder.to_bytes | Serialize the binder's annotations into a byte string. | spacy/tokens/_serialize.py | def to_bytes(self):
"""Serialize the binder's annotations into a byte string."""
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
lengths = [len(tokens) for tokens in self.tokens]
msg = {
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for tokens in self.tokens:
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lengths = [len(tokens) for tokens in self.tokens]
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train | Binder.from_bytes | Deserialize the binder's annotations from a byte string. | spacy/tokens/_serialize.py | def from_bytes(self, string):
"""Deserialize the binder's annotations from a byte string."""
msg = srsly.msgpack_loads(gzip.decompress(string))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
lengths = numpy.fromstring(msg["lengths"], dtype="int32")
flat_spac... | def from_bytes(self, string):
"""Deserialize the binder's annotations from a byte string."""
msg = srsly.msgpack_loads(gzip.decompress(string))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
lengths = numpy.fromstring(msg["lengths"], dtype="int32")
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train | load_data | Load data from the IMDB dataset. | examples/training/train_textcat.py | def load_data(limit=0, split=0.8):
"""Load data from the IMDB dataset."""
# Partition off part of the train data for evaluation
train_data, _ = thinc.extra.datasets.imdb()
random.shuffle(train_data)
train_data = train_data[-limit:]
texts, labels = zip(*train_data)
cats = [{"POSITIVE": bool(y... | def load_data(limit=0, split=0.8):
"""Load data from the IMDB dataset."""
# Partition off part of the train data for evaluation
train_data, _ = thinc.extra.datasets.imdb()
random.shuffle(train_data)
train_data = train_data[-limit:]
texts, labels = zip(*train_data)
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train | package | Generate Python package for model data, including meta and required
installation files. A new directory will be created in the specified
output directory, and model data will be copied over. If --create-meta is
set and a meta.json already exists in the output directory, the existing
values will be used ... | spacy/cli/package.py | def package(input_dir, output_dir, meta_path=None, create_meta=False, force=False):
"""
Generate Python package for model data, including meta and required
installation files. A new directory will be created in the specified
output directory, and model data will be copied over. If --create-meta is
s... | def package(input_dir, output_dir, meta_path=None, create_meta=False, force=False):
"""
Generate Python package for model data, including meta and required
installation files. A new directory will be created in the specified
output directory, and model data will be copied over. If --create-meta is
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train | FrenchLemmatizer.is_base_form | Check whether we're dealing with an uninflected paradigm, so we can
avoid lemmatization entirely. | spacy/lang/fr/lemmatizer/lemmatizer.py | def is_base_form(self, univ_pos, morphology=None):
"""
Check whether we're dealing with an uninflected paradigm, so we can
avoid lemmatization entirely.
"""
morphology = {} if morphology is None else morphology
others = [key for key in morphology
if key ... | def is_base_form(self, univ_pos, morphology=None):
"""
Check whether we're dealing with an uninflected paradigm, so we can
avoid lemmatization entirely.
"""
morphology = {} if morphology is None else morphology
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train | main | Set up the pipeline and entity recognizer, and train the new entity. | examples/training/train_new_entity_type.py | def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
"""Set up the pipeline and entity recognizer, and train the new entity."""
random.seed(0)
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
... | def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
"""Set up the pipeline and entity recognizer, and train the new entity."""
random.seed(0)
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
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train | conll_ner2json | Convert files in the CoNLL-2003 NER format into JSON format for use with
train cli. | spacy/cli/converters/conll_ner2json.py | def conll_ner2json(input_data, **kwargs):
"""
Convert files in the CoNLL-2003 NER format into JSON format for use with
train cli.
"""
delimit_docs = "-DOCSTART- -X- O O"
output_docs = []
for doc in input_data.strip().split(delimit_docs):
doc = doc.strip()
if not doc:
... | def conll_ner2json(input_data, **kwargs):
"""
Convert files in the CoNLL-2003 NER format into JSON format for use with
train cli.
"""
delimit_docs = "-DOCSTART- -X- O O"
output_docs = []
for doc in input_data.strip().split(delimit_docs):
doc = doc.strip()
if not doc:
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train | main | Create a new model, set up the pipeline and train the tagger. In order to
train the tagger with a custom tag map, we're creating a new Language
instance with a custom vocab. | examples/training/train_tagger.py | def main(lang="en", output_dir=None, n_iter=25):
"""Create a new model, set up the pipeline and train the tagger. In order to
train the tagger with a custom tag map, we're creating a new Language
instance with a custom vocab.
"""
nlp = spacy.blank(lang)
# add the tagger to the pipeline
# nlp... | def main(lang="en", output_dir=None, n_iter=25):
"""Create a new model, set up the pipeline and train the tagger. In order to
train the tagger with a custom tag map, we're creating a new Language
instance with a custom vocab.
"""
nlp = spacy.blank(lang)
# add the tagger to the pipeline
# nlp... | [
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train | load_textcat_data | Load data from the IMDB dataset. | examples/training/pretrain_textcat.py | def load_textcat_data(limit=0):
"""Load data from the IMDB dataset."""
# Partition off part of the train data for evaluation
train_data, eval_data = thinc.extra.datasets.imdb()
random.shuffle(train_data)
train_data = train_data[-limit:]
texts, labels = zip(*train_data)
eval_texts, eval_label... | def load_textcat_data(limit=0):
"""Load data from the IMDB dataset."""
# Partition off part of the train data for evaluation
train_data, eval_data = thinc.extra.datasets.imdb()
random.shuffle(train_data)
train_data = train_data[-limit:]
texts, labels = zip(*train_data)
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train | init_model | Create a new model from raw data, like word frequencies, Brown clusters
and word vectors. If vectors are provided in Word2Vec format, they can
be either a .txt or zipped as a .zip or .tar.gz. | spacy/cli/init_model.py | def init_model(
lang,
output_dir,
freqs_loc=None,
clusters_loc=None,
jsonl_loc=None,
vectors_loc=None,
prune_vectors=-1,
):
"""
Create a new model from raw data, like word frequencies, Brown clusters
and word vectors. If vectors are provided in Word2Vec format, they can
be ei... | def init_model(
lang,
output_dir,
freqs_loc=None,
clusters_loc=None,
jsonl_loc=None,
vectors_loc=None,
prune_vectors=-1,
):
"""
Create a new model from raw data, like word frequencies, Brown clusters
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train | open_file | Handle .gz, .tar.gz or unzipped files | spacy/cli/init_model.py | def open_file(loc):
"""Handle .gz, .tar.gz or unzipped files"""
loc = ensure_path(loc)
if tarfile.is_tarfile(str(loc)):
return tarfile.open(str(loc), "r:gz")
elif loc.parts[-1].endswith("gz"):
return (line.decode("utf8") for line in gzip.open(str(loc), "r"))
elif loc.parts[-1].endswi... | def open_file(loc):
"""Handle .gz, .tar.gz or unzipped files"""
loc = ensure_path(loc)
if tarfile.is_tarfile(str(loc)):
return tarfile.open(str(loc), "r:gz")
elif loc.parts[-1].endswith("gz"):
return (line.decode("utf8") for line in gzip.open(str(loc), "r"))
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train | main | Load the model, set up the pipeline and train the entity recognizer. | examples/training/train_ner.py | def main(model=None, output_dir=None, n_iter=100):
"""Load the model, set up the pipeline and train the entity recognizer."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank La... | def main(model=None, output_dir=None, n_iter=100):
"""Load the model, set up the pipeline and train the entity recognizer."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank La... | [
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train | pretrain | Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
using an approximate language-modelling objective. Specifically, we load
pre-trained vectors, and train a component like a CNN, BiLSTM, etc to predict
vectors which match the pre-trained ones. The weights are saved to a directory
af... | spacy/cli/pretrain.py | def pretrain(
texts_loc,
vectors_model,
output_dir,
width=96,
depth=4,
embed_rows=2000,
loss_func="cosine",
use_vectors=False,
dropout=0.2,
n_iter=1000,
batch_size=3000,
max_length=500,
min_length=5,
seed=0,
n_save_every=None,
):
"""
Pre-train the 'tok... | def pretrain(
texts_loc,
vectors_model,
output_dir,
width=96,
depth=4,
embed_rows=2000,
loss_func="cosine",
use_vectors=False,
dropout=0.2,
n_iter=1000,
batch_size=3000,
max_length=500,
min_length=5,
seed=0,
n_save_every=None,
):
"""
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train | make_update | Perform an update over a single batch of documents.
docs (iterable): A batch of `Doc` objects.
drop (float): The droput rate.
optimizer (callable): An optimizer.
RETURNS loss: A float for the loss. | spacy/cli/pretrain.py | def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
"""Perform an update over a single batch of documents.
docs (iterable): A batch of `Doc` objects.
drop (float): The droput rate.
optimizer (callable): An optimizer.
RETURNS loss: A float for the loss.
"""
predictions, backpr... | def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
"""Perform an update over a single batch of documents.
docs (iterable): A batch of `Doc` objects.
drop (float): The droput rate.
optimizer (callable): An optimizer.
RETURNS loss: A float for the loss.
"""
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train | get_vectors_loss | Compute a mean-squared error loss between the documents' vectors and
the prediction.
Note that this is ripe for customization! We could compute the vectors
in some other word, e.g. with an LSTM language model, or use some other
type of objective. | spacy/cli/pretrain.py | def get_vectors_loss(ops, docs, prediction, objective="L2"):
"""Compute a mean-squared error loss between the documents' vectors and
the prediction.
Note that this is ripe for customization! We could compute the vectors
in some other word, e.g. with an LSTM language model, or use some other
type of... | def get_vectors_loss(ops, docs, prediction, objective="L2"):
"""Compute a mean-squared error loss between the documents' vectors and
the prediction.
Note that this is ripe for customization! We could compute the vectors
in some other word, e.g. with an LSTM language model, or use some other
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train | create_pretraining_model | Define a network for the pretraining. We simply add an output layer onto
the tok2vec input model. The tok2vec input model needs to be a model that
takes a batch of Doc objects (as a list), and returns a list of arrays.
Each array in the output needs to have one row per token in the doc. | spacy/cli/pretrain.py | def create_pretraining_model(nlp, tok2vec):
"""Define a network for the pretraining. We simply add an output layer onto
the tok2vec input model. The tok2vec input model needs to be a model that
takes a batch of Doc objects (as a list), and returns a list of arrays.
Each array in the output needs to have... | def create_pretraining_model(nlp, tok2vec):
"""Define a network for the pretraining. We simply add an output layer onto
the tok2vec input model. The tok2vec input model needs to be a model that
takes a batch of Doc objects (as a list), and returns a list of arrays.
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train | _smart_round | Round large numbers as integers, smaller numbers as decimals. | spacy/cli/pretrain.py | def _smart_round(figure, width=10, max_decimal=4):
"""Round large numbers as integers, smaller numbers as decimals."""
n_digits = len(str(int(figure)))
n_decimal = width - (n_digits + 1)
if n_decimal <= 1:
return str(int(figure))
else:
n_decimal = min(n_decimal, max_decimal)
... | def _smart_round(figure, width=10, max_decimal=4):
"""Round large numbers as integers, smaller numbers as decimals."""
n_digits = len(str(int(figure)))
n_decimal = width - (n_digits + 1)
if n_decimal <= 1:
return str(int(figure))
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train | noun_chunks | Detect base noun phrases. Works on both Doc and Span. | spacy/lang/el/syntax_iterators.py | def noun_chunks(obj):
"""
Detect base noun phrases. Works on both Doc and Span.
"""
# It follows the logic of the noun chunks finder of English language,
# adjusted to some Greek language special characteristics.
# obj tag corrects some DEP tagger mistakes.
# Further improvement of the model... | def noun_chunks(obj):
"""
Detect base noun phrases. Works on both Doc and Span.
"""
# It follows the logic of the noun chunks finder of English language,
# adjusted to some Greek language special characteristics.
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train | get_ext_args | Validate and convert arguments. Reused in Doc, Token and Span. | spacy/tokens/underscore.py | def get_ext_args(**kwargs):
"""Validate and convert arguments. Reused in Doc, Token and Span."""
default = kwargs.get("default")
getter = kwargs.get("getter")
setter = kwargs.get("setter")
method = kwargs.get("method")
if getter is None and setter is not None:
raise ValueError(Errors.E08... | def get_ext_args(**kwargs):
"""Validate and convert arguments. Reused in Doc, Token and Span."""
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getter = kwargs.get("getter")
setter = kwargs.get("setter")
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train | is_writable_attr | Check if an extension attribute is writable.
ext (tuple): The (default, getter, setter, method) tuple available via
{Doc,Span,Token}.get_extension.
RETURNS (bool): Whether the attribute is writable. | spacy/tokens/underscore.py | def is_writable_attr(ext):
"""Check if an extension attribute is writable.
ext (tuple): The (default, getter, setter, method) tuple available via
{Doc,Span,Token}.get_extension.
RETURNS (bool): Whether the attribute is writable.
"""
default, method, getter, setter = ext
# Extension is w... | def is_writable_attr(ext):
"""Check if an extension attribute is writable.
ext (tuple): The (default, getter, setter, method) tuple available via
{Doc,Span,Token}.get_extension.
RETURNS (bool): Whether the attribute is writable.
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default, method, getter, setter = ext
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train | is_new_osx | Check whether we're on OSX >= 10.10 | setup.py | def is_new_osx():
"""Check whether we're on OSX >= 10.10"""
name = distutils.util.get_platform()
if sys.platform != "darwin":
return False
elif name.startswith("macosx-10"):
minor_version = int(name.split("-")[1].split(".")[1])
if minor_version >= 7:
return True
... | def is_new_osx():
"""Check whether we're on OSX >= 10.10"""
name = distutils.util.get_platform()
if sys.platform != "darwin":
return False
elif name.startswith("macosx-10"):
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train | get_position_label | Return labels indicating the position of the word in the document. | examples/training/ner_multitask_objective.py | def get_position_label(i, words, tags, heads, labels, ents):
"""Return labels indicating the position of the word in the document.
"""
if len(words) < 20:
return "short-doc"
elif i == 0:
return "first-word"
elif i < 10:
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elif i < 20:
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"""Return labels indicating the position of the word in the document.
"""
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train | download | Download compatible model from default download path using pip. Model
can be shortcut, model name or, if --direct flag is set, full model name
with version. For direct downloads, the compatibility check will be skipped. | spacy/cli/download.py | def download(model, direct=False, *pip_args):
"""
Download compatible model from default download path using pip. Model
can be shortcut, model name or, if --direct flag is set, full model name
with version. For direct downloads, the compatibility check will be skipped.
"""
dl_tpl = "{m}-{v}/{m}-... | def download(model, direct=False, *pip_args):
"""
Download compatible model from default download path using pip. Model
can be shortcut, model name or, if --direct flag is set, full model name
with version. For direct downloads, the compatibility check will be skipped.
"""
dl_tpl = "{m}-{v}/{m}-... | [
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train | convert | Convert files into JSON format for use with train command and other
experiment management functions. If no output_dir is specified, the data
is written to stdout, so you can pipe them forward to a JSON file:
$ spacy convert some_file.conllu > some_file.json | spacy/cli/convert.py | def convert(
input_file,
output_dir="-",
file_type="json",
n_sents=1,
morphology=False,
converter="auto",
lang=None,
):
"""
Convert files into JSON format for use with train command and other
experiment management functions. If no output_dir is specified, the data
is written ... | def convert(
input_file,
output_dir="-",
file_type="json",
n_sents=1,
morphology=False,
converter="auto",
lang=None,
):
"""
Convert files into JSON format for use with train command and other
experiment management functions. If no output_dir is specified, the data
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train | load_model | Load a specific spaCy model | bin/ud/run_eval.py | def load_model(modelname, add_sentencizer=False):
""" Load a specific spaCy model """
loading_start = time.time()
nlp = spacy.load(modelname)
if add_sentencizer:
nlp.add_pipe(nlp.create_pipe('sentencizer'))
loading_end = time.time()
loading_time = loading_end - loading_start
if add_s... | def load_model(modelname, add_sentencizer=False):
""" Load a specific spaCy model """
loading_start = time.time()
nlp = spacy.load(modelname)
if add_sentencizer:
nlp.add_pipe(nlp.create_pipe('sentencizer'))
loading_end = time.time()
loading_time = loading_end - loading_start
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train | load_default_model_sentencizer | Load a generic spaCy model and add the sentencizer for sentence tokenization | bin/ud/run_eval.py | def load_default_model_sentencizer(lang):
""" Load a generic spaCy model and add the sentencizer for sentence tokenization"""
loading_start = time.time()
lang_class = get_lang_class(lang)
nlp = lang_class()
nlp.add_pipe(nlp.create_pipe('sentencizer'))
loading_end = time.time()
loading_time =... | def load_default_model_sentencizer(lang):
""" Load a generic spaCy model and add the sentencizer for sentence tokenization"""
loading_start = time.time()
lang_class = get_lang_class(lang)
nlp = lang_class()
nlp.add_pipe(nlp.create_pipe('sentencizer'))
loading_end = time.time()
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train | get_freq_tuples | Turn a list of errors into frequency-sorted tuples thresholded by a certain total number | bin/ud/run_eval.py | def get_freq_tuples(my_list, print_total_threshold):
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d = {}
for token in my_list:
d.setdefault(token, 0)
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train | _contains_blinded_text | Heuristic to determine whether the treebank has blinded texts or not | bin/ud/run_eval.py | def _contains_blinded_text(stats_xml):
""" Heuristic to determine whether the treebank has blinded texts or not """
tree = ET.parse(stats_xml)
root = tree.getroot()
total_tokens = int(root.find('size/total/tokens').text)
unique_lemmas = int(root.find('lemmas').get('unique'))
# assume the corpus... | def _contains_blinded_text(stats_xml):
""" Heuristic to determine whether the treebank has blinded texts or not """
tree = ET.parse(stats_xml)
root = tree.getroot()
total_tokens = int(root.find('size/total/tokens').text)
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train | fetch_all_treebanks | Fetch the txt files for all treebanks for a given set of languages | bin/ud/run_eval.py | def fetch_all_treebanks(ud_dir, languages, corpus, best_per_language):
"""" Fetch the txt files for all treebanks for a given set of languages """
all_treebanks = dict()
treebank_size = dict()
for l in languages:
all_treebanks[l] = []
treebank_size[l] = 0
for treebank_dir in ud_dir.... | def fetch_all_treebanks(ud_dir, languages, corpus, best_per_language):
"""" Fetch the txt files for all treebanks for a given set of languages """
all_treebanks = dict()
treebank_size = dict()
for l in languages:
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train | run_single_eval | Run an evaluation of a model nlp on a certain specified treebank | bin/ud/run_eval.py | def run_single_eval(nlp, loading_time, print_name, text_path, gold_ud, tmp_output_path, out_file, print_header,
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"""" Run an evaluation of a model nlp on a certain specified treebank """
with text_path.open(mode='r', encoding='utf-8') as f:
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"""" Run an evaluation of a model nlp on a certain specified treebank """
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train | run_all_evals | Run an evaluation for each language with its specified models and treebanks | bin/ud/run_eval.py | def run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks):
"""" Run an evaluation for each language with its specified models and treebanks """
print_header = True
for tb_lang, treebank_list in treebanks.items():
print()
print("Language", tb_lang)
for text_path i... | def run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks):
"""" Run an evaluation for each language with its specified models and treebanks """
print_header = True
for tb_lang, treebank_list in treebanks.items():
print()
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train | main | Assemble all treebanks and models to run evaluations with.
When setting check_parse to True, the default models will not be evaluated as they don't have parsing functionality | bin/ud/run_eval.py | def main(out_path, ud_dir, check_parse=False, langs=ALL_LANGUAGES, exclude_trained_models=False, exclude_multi=False,
hide_freq=False, corpus='train', best_per_language=False):
""""
Assemble all treebanks and models to run evaluations with.
When setting check_parse to True, the default models will ... | def main(out_path, ud_dir, check_parse=False, langs=ALL_LANGUAGES, exclude_trained_models=False, exclude_multi=False,
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train | noun_chunks | Detect base noun phrases from a dependency parse. Works on both Doc and Span. | spacy/lang/de/syntax_iterators.py | def noun_chunks(obj):
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
# this iterator extracts spans headed by NOUNs starting from the left-most
# syntactic dependent until the NOUN itself for close apposition and
# measurement construction, the span is some... | def noun_chunks(obj):
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
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train | with_cpu | Wrap a model that should run on CPU, transferring inputs and outputs
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"""Wrap a model that should run on CPU, transferring inputs and outputs
as necessary."""
model.to_cpu()
def with_cpu_forward(inputs, drop=0.0):
cpu_outputs, backprop = model.begin_update(_to_cpu(inputs), drop=drop)
gpu_outputs = _to_device(ops, cpu_outputs)
... | def with_cpu(ops, model):
"""Wrap a model that should run on CPU, transferring inputs and outputs
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model.to_cpu()
def with_cpu_forward(inputs, drop=0.0):
cpu_outputs, backprop = model.begin_update(_to_cpu(inputs), drop=drop)
gpu_outputs = _to_device(ops, cpu_outputs)
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"""
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If exclusive_classes=True, a softmax non-linearity is applied, so that the
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train | concatenate_lists | Compose two or more models `f`, `g`, etc, such that their outputs are
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"""Compose two or more models `f`, `g`, etc, such that their outputs are
concatenated, i.e. `concatenate(f, g)(x)` computes `hstack(f(x), g(x))`
"""
if not layers:
return noop()
drop_factor = kwargs.get("drop_factor", 1.0)
ops... | def concatenate_lists(*layers, **kwargs): # pragma: no cover
"""Compose two or more models `f`, `g`, etc, such that their outputs are
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if not layers:
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drop_factor = kwargs.get("drop_factor", 1.0)
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train | masked_language_model | Convert a model into a BERT-style masked language model | spacy/_ml.py | def masked_language_model(vocab, model, mask_prob=0.15):
"""Convert a model into a BERT-style masked language model"""
random_words = _RandomWords(vocab)
def mlm_forward(docs, drop=0.0):
mask, docs = _apply_mask(docs, random_words, mask_prob=mask_prob)
mask = model.ops.asarray(mask).reshap... | def masked_language_model(vocab, model, mask_prob=0.15):
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random_words = _RandomWords(vocab)
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train | SimilarityHook.begin_training | Allocate model, using width from tensorizer in pipeline.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of. | spacy/pipeline/hooks.py | def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
"""Allocate model, using width from tensorizer in pipeline.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
"""
if self.model is True:
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train | DependencyRenderer.render | Render complete markup.
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered SVG or HTML markup. | spacy/displacy/render.py | def render(self, parsed, page=False, minify=False):
"""Render complete markup.
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered SVG or HTML markup.
"""
... | def render(self, parsed, page=False, minify=False):
"""Render complete markup.
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered SVG or HTML markup.
"""
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train | DependencyRenderer.render_svg | Render SVG.
render_id (int): Unique ID, typically index of document.
words (list): Individual words and their tags.
arcs (list): Individual arcs and their start, end, direction and label.
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render_id (int): Unique ID, typically index of document.
words (list): Individual words and their tags.
arcs (list): Individual arcs and their start, end, direction and label.
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words (list): Individual words and their tags.
arcs (list): Individual arcs and their start, end, direction and label.
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train | DependencyRenderer.render_word | Render individual word.
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tag (unicode): Part-of-speech tag.
i (int): Unique ID, typically word index.
RETURNS (unicode): Rendered SVG markup. | spacy/displacy/render.py | def render_word(self, text, tag, i):
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train | DependencyRenderer.render_arrow | Render individual arrow.
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end (int): Index of end word.
direction (unicode): Arrow direction, 'left' or 'right'.
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end (int): Index of end word.
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train | DependencyRenderer.get_arc | Render individual arc.
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y (int): Y-coordinate of arrow start and end point.
y_curve (int): Y-corrdinate of Cubic Bézier y_curve point.
x_end (int): X-coordinate of arrow end point.
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y (int): Y-coordinate of arrow start and end point.
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train | DependencyRenderer.get_arrowhead | Render individual arrow head.
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x (int): X-coordinate of arrow start point.
y (int): Y-coordinate of arrow start and end point.
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args (list): Individual arcs and their start, end, direction and label.
RETURNS (list): Arc levels sorted from lowest to highest. | spacy/displacy/render.py | def get_levels(self, arcs):
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args (list): Individual arcs and their start, end, direction and label.
RETURNS (list): Arc levels sorted from lowest to highest.
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train | EntityRenderer.render | Render complete markup.
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered HTML markup. | spacy/displacy/render.py | def render(self, parsed, page=False, minify=False):
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parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
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render... | def render(self, parsed, page=False, minify=False):
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minify (bool): Minify HTML markup.
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train | EntityRenderer.render_ents | Render entities in text.
text (unicode): Original text.
spans (list): Individual entity spans and their start, end and label.
title (unicode or None): Document title set in Doc.user_data['title']. | spacy/displacy/render.py | def render_ents(self, text, spans, title):
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text (unicode): Original text.
spans (list): Individual entity spans and their start, end and label.
title (unicode or None): Document title set in Doc.user_data['title'].
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spans (list): Individual entity spans and their start, end and label.
title (unicode or None): Document title set in Doc.user_data['title'].
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train | merge_noun_chunks | Merge noun chunks into a single token.
doc (Doc): The Doc object.
RETURNS (Doc): The Doc object with merged noun chunks.
DOCS: https://spacy.io/api/pipeline-functions#merge_noun_chunks | spacy/pipeline/functions.py | def merge_noun_chunks(doc):
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train | merge_entities | Merge entities into a single token.
doc (Doc): The Doc object.
RETURNS (Doc): The Doc object with merged entities.
DOCS: https://spacy.io/api/pipeline-functions#merge_entities | spacy/pipeline/functions.py | def merge_entities(doc):
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train | merge_subtokens | Merge subtokens into a single token.
doc (Doc): The Doc object.
label (unicode): The subtoken dependency label.
RETURNS (Doc): The Doc object with merged subtokens.
DOCS: https://spacy.io/api/pipeline-functions#merge_subtokens | spacy/pipeline/functions.py | def merge_subtokens(doc, label="subtok"):
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label (unicode): The subtoken dependency label.
RETURNS (Doc): The Doc object with merged subtokens.
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merger = Match... | def merge_subtokens(doc, label="subtok"):
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doc (Doc): The Doc object.
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train | train | Train or update a spaCy model. Requires data to be formatted in spaCy's
JSON format. To convert data from other formats, use the `spacy convert`
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n_iter=30,
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meta_path=None,
init_tok2vec=None,
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train | _score_for_model | Returns mean score between tasks in pipeline that can be used for early stopping. | spacy/cli/train.py | def _score_for_model(meta):
""" Returns mean score between tasks in pipeline that can be used for early stopping. """
mean_acc = list()
pipes = meta["pipeline"]
acc = meta["accuracy"]
if "tagger" in pipes:
mean_acc.append(acc["tags_acc"])
if "parser" in pipes:
mean_acc.append((ac... | def _score_for_model(meta):
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train | _load_pretrained_tok2vec | Load pre-trained weights for the 'token-to-vector' part of the component
models, which is typically a CNN. See 'spacy pretrain'. Experimental. | spacy/cli/train.py | def _load_pretrained_tok2vec(nlp, loc):
"""Load pre-trained weights for the 'token-to-vector' part of the component
models, which is typically a CNN. See 'spacy pretrain'. Experimental.
"""
with loc.open("rb") as file_:
weights_data = file_.read()
loaded = []
for name, component in nlp.p... | def _load_pretrained_tok2vec(nlp, loc):
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models, which is typically a CNN. See 'spacy pretrain'. Experimental.
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train | conllu2json | Convert conllu files into JSON format for use with train cli.
use_morphology parameter enables appending morphology to tags, which is
useful for languages such as Spanish, where UD tags are not so rich.
Extract NER tags if available and convert them so that they follow
BILUO and the Wikipedia scheme | spacy/cli/converters/conllu2json.py | def conllu2json(input_data, n_sents=10, use_morphology=False, lang=None):
"""
Convert conllu files into JSON format for use with train cli.
use_morphology parameter enables appending morphology to tags, which is
useful for languages such as Spanish, where UD tags are not so rich.
Extract NER tags i... | def conllu2json(input_data, n_sents=10, use_morphology=False, lang=None):
"""
Convert conllu files into JSON format for use with train cli.
use_morphology parameter enables appending morphology to tags, which is
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train | is_ner | Check the 10th column of the first token to determine if the file contains
NER tags | spacy/cli/converters/conllu2json.py | def is_ner(tag):
"""
Check the 10th column of the first token to determine if the file contains
NER tags
"""
tag_match = re.match("([A-Z_]+)-([A-Z_]+)", tag)
if tag_match:
return True
elif tag == "O":
return True
else:
return False | def is_ner(tag):
"""
Check the 10th column of the first token to determine if the file contains
NER tags
"""
tag_match = re.match("([A-Z_]+)-([A-Z_]+)", tag)
if tag_match:
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train | simplify_tags | Simplify tags obtained from the dataset in order to follow Wikipedia
scheme (PER, LOC, ORG, MISC). 'PER', 'LOC' and 'ORG' keep their tags, while
'GPE_LOC' is simplified to 'LOC', 'GPE_ORG' to 'ORG' and all remaining tags to
'MISC'. | spacy/cli/converters/conllu2json.py | def simplify_tags(iob):
"""
Simplify tags obtained from the dataset in order to follow Wikipedia
scheme (PER, LOC, ORG, MISC). 'PER', 'LOC' and 'ORG' keep their tags, while
'GPE_LOC' is simplified to 'LOC', 'GPE_ORG' to 'ORG' and all remaining tags to
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"""
new_iob = []
for tag in ... | def simplify_tags(iob):
"""
Simplify tags obtained from the dataset in order to follow Wikipedia
scheme (PER, LOC, ORG, MISC). 'PER', 'LOC' and 'ORG' keep their tags, while
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train | info | Print info about spaCy installation. If a model shortcut link is
speficied as an argument, print model information. Flag --markdown
prints details in Markdown for easy copy-pasting to GitHub issues. | spacy/cli/info.py | def info(model=None, markdown=False, silent=False):
"""
Print info about spaCy installation. If a model shortcut link is
speficied as an argument, print model information. Flag --markdown
prints details in Markdown for easy copy-pasting to GitHub issues.
"""
msg = Printer()
if model:
... | def info(model=None, markdown=False, silent=False):
"""
Print info about spaCy installation. If a model shortcut link is
speficied as an argument, print model information. Flag --markdown
prints details in Markdown for easy copy-pasting to GitHub issues.
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msg = Printer()
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train | print_markdown | Print data in GitHub-flavoured Markdown format for issues etc.
data (dict or list of tuples): Label/value pairs.
title (unicode or None): Title, will be rendered as headline 2. | spacy/cli/info.py | def print_markdown(data, title=None):
"""Print data in GitHub-flavoured Markdown format for issues etc.
data (dict or list of tuples): Label/value pairs.
title (unicode or None): Title, will be rendered as headline 2.
"""
markdown = []
for key, value in data.items():
if isinstance(value... | def print_markdown(data, title=None):
"""Print data in GitHub-flavoured Markdown format for issues etc.
data (dict or list of tuples): Label/value pairs.
title (unicode or None): Title, will be rendered as headline 2.
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train | main | Load the model, set up the pipeline and train the parser. | examples/training/train_intent_parser.py | def main(model=None, output_dir=None, n_iter=15):
"""Load the model, set up the pipeline and train the parser."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank Language class... | def main(model=None, output_dir=None, n_iter=15):
"""Load the model, set up the pipeline and train the parser."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
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train | Language.get_pipe | Get a pipeline component for a given component name.
name (unicode): Name of pipeline component to get.
RETURNS (callable): The pipeline component.
DOCS: https://spacy.io/api/language#get_pipe | spacy/language.py | def get_pipe(self, name):
"""Get a pipeline component for a given component name.
name (unicode): Name of pipeline component to get.
RETURNS (callable): The pipeline component.
DOCS: https://spacy.io/api/language#get_pipe
"""
for pipe_name, component in self.pipeline:
... | def get_pipe(self, name):
"""Get a pipeline component for a given component name.
name (unicode): Name of pipeline component to get.
RETURNS (callable): The pipeline component.
DOCS: https://spacy.io/api/language#get_pipe
"""
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train | Language.create_pipe | Create a pipeline component from a factory.
name (unicode): Factory name to look up in `Language.factories`.
config (dict): Configuration parameters to initialise component.
RETURNS (callable): Pipeline component.
DOCS: https://spacy.io/api/language#create_pipe | spacy/language.py | def create_pipe(self, name, config=dict()):
"""Create a pipeline component from a factory.
name (unicode): Factory name to look up in `Language.factories`.
config (dict): Configuration parameters to initialise component.
RETURNS (callable): Pipeline component.
DOCS: https://spa... | def create_pipe(self, name, config=dict()):
"""Create a pipeline component from a factory.
name (unicode): Factory name to look up in `Language.factories`.
config (dict): Configuration parameters to initialise component.
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train | Language.add_pipe | Add a component to the processing pipeline. Valid components are
callables that take a `Doc` object, modify it and return it. Only one
of before/after/first/last can be set. Default behaviour is "last".
component (callable): The pipeline component.
name (unicode): Name of pipeline compo... | spacy/language.py | def add_pipe(
self, component, name=None, before=None, after=None, first=None, last=None
):
"""Add a component to the processing pipeline. Valid components are
callables that take a `Doc` object, modify it and return it. Only one
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self, component, name=None, before=None, after=None, first=None, last=None
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"""Add a component to the processing pipeline. Valid components are
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train | Language.replace_pipe | Replace a component in the pipeline.
name (unicode): Name of the component to replace.
component (callable): Pipeline component.
DOCS: https://spacy.io/api/language#replace_pipe | spacy/language.py | def replace_pipe(self, name, component):
"""Replace a component in the pipeline.
name (unicode): Name of the component to replace.
component (callable): Pipeline component.
DOCS: https://spacy.io/api/language#replace_pipe
"""
if name not in self.pipe_names:
... | def replace_pipe(self, name, component):
"""Replace a component in the pipeline.
name (unicode): Name of the component to replace.
component (callable): Pipeline component.
DOCS: https://spacy.io/api/language#replace_pipe
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train | Language.rename_pipe | Rename a pipeline component.
old_name (unicode): Name of the component to rename.
new_name (unicode): New name of the component.
DOCS: https://spacy.io/api/language#rename_pipe | spacy/language.py | def rename_pipe(self, old_name, new_name):
"""Rename a pipeline component.
old_name (unicode): Name of the component to rename.
new_name (unicode): New name of the component.
DOCS: https://spacy.io/api/language#rename_pipe
"""
if old_name not in self.pipe_names:
... | def rename_pipe(self, old_name, new_name):
"""Rename a pipeline component.
old_name (unicode): Name of the component to rename.
new_name (unicode): New name of the component.
DOCS: https://spacy.io/api/language#rename_pipe
"""
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train | Language.remove_pipe | Remove a component from the pipeline.
name (unicode): Name of the component to remove.
RETURNS (tuple): A `(name, component)` tuple of the removed component.
DOCS: https://spacy.io/api/language#remove_pipe | spacy/language.py | def remove_pipe(self, name):
"""Remove a component from the pipeline.
name (unicode): Name of the component to remove.
RETURNS (tuple): A `(name, component)` tuple of the removed component.
DOCS: https://spacy.io/api/language#remove_pipe
"""
if name not in self.pipe_nam... | def remove_pipe(self, name):
"""Remove a component from the pipeline.
name (unicode): Name of the component to remove.
RETURNS (tuple): A `(name, component)` tuple of the removed component.
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train | Language.update | Update the models in the pipeline.
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
drop (float): The droput rate.
sgd (callable): An optimizer.
RETURNS (dict): Results from the update.
DOCS: https://spacy.io/api/language#upda... | spacy/language.py | def update(self, docs, golds, drop=0.0, sgd=None, losses=None, component_cfg=None):
"""Update the models in the pipeline.
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
drop (float): The droput rate.
sgd (callable): An optimizer.
... | def update(self, docs, golds, drop=0.0, sgd=None, losses=None, component_cfg=None):
"""Update the models in the pipeline.
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
drop (float): The droput rate.
sgd (callable): An optimizer.
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train | Language.rehearse | Make a "rehearsal" update to the models in the pipeline, to prevent
forgetting. Rehearsal updates run an initial copy of the model over some
data, and update the model so its current predictions are more like the
initial ones. This is useful for keeping a pre-trained model on-track,
even... | spacy/language.py | def rehearse(self, docs, sgd=None, losses=None, config=None):
"""Make a "rehearsal" update to the models in the pipeline, to prevent
forgetting. Rehearsal updates run an initial copy of the model over some
data, and update the model so its current predictions are more like the
initial on... | def rehearse(self, docs, sgd=None, losses=None, config=None):
"""Make a "rehearsal" update to the models in the pipeline, to prevent
forgetting. Rehearsal updates run an initial copy of the model over some
data, and update the model so its current predictions are more like the
initial on... | [
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train | Language.preprocess_gold | Can be called before training to pre-process gold data. By default,
it handles nonprojectivity and adds missing tags to the tag map.
docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects.
YIELDS (tuple): Tuples of preprocessed `Doc` and `GoldParse` objects. | spacy/language.py | def preprocess_gold(self, docs_golds):
"""Can be called before training to pre-process gold data. By default,
it handles nonprojectivity and adds missing tags to the tag map.
docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects.
YIELDS (tuple): Tuples of preprocessed `Doc` and... | def preprocess_gold(self, docs_golds):
"""Can be called before training to pre-process gold data. By default,
it handles nonprojectivity and adds missing tags to the tag map.
docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects.
YIELDS (tuple): Tuples of preprocessed `Doc` and... | [
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train | Language.begin_training | Allocate models, pre-process training data and acquire a trainer and
optimizer. Used as a contextmanager.
get_gold_tuples (function): Function returning gold data
component_cfg (dict): Config parameters for specific components.
**cfg: Config parameters.
RETURNS: An optimizer.
... | spacy/language.py | def begin_training(self, get_gold_tuples=None, sgd=None, component_cfg=None, **cfg):
"""Allocate models, pre-process training data and acquire a trainer and
optimizer. Used as a contextmanager.
get_gold_tuples (function): Function returning gold data
component_cfg (dict): Config paramet... | def begin_training(self, get_gold_tuples=None, sgd=None, component_cfg=None, **cfg):
"""Allocate models, pre-process training data and acquire a trainer and
optimizer. Used as a contextmanager.
get_gold_tuples (function): Function returning gold data
component_cfg (dict): Config paramet... | [
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train | Language.resume_training | Continue training a pre-trained model.
Create and return an optimizer, and initialize "rehearsal" for any pipeline
component that has a .rehearse() method. Rehearsal is used to prevent
models from "forgetting" their initialised "knowledge". To perform
rehearsal, collect samples of text ... | spacy/language.py | def resume_training(self, sgd=None, **cfg):
"""Continue training a pre-trained model.
Create and return an optimizer, and initialize "rehearsal" for any pipeline
component that has a .rehearse() method. Rehearsal is used to prevent
models from "forgetting" their initialised "knowledge".... | def resume_training(self, sgd=None, **cfg):
"""Continue training a pre-trained model.
Create and return an optimizer, and initialize "rehearsal" for any pipeline
component that has a .rehearse() method. Rehearsal is used to prevent
models from "forgetting" their initialised "knowledge".... | [
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] | explosion/spaCy | python | https://github.com/explosion/spaCy/blob/8ee4100f8ffb336886208a1ea827bf4c745e2709/spacy/language.py#L569-L591 | [
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